Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2012-2013

C O N T E N TS
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■ History—

Message from Director-General

We are starting to see revolutions occurring in a wide variety of areas,from cutting-edge R&D to business, in what is often called a “fourth par­adigm”. Over the last year or two, we have seen big data (referring tomassive data sets that are of highly variable quality) emerging as an R&D
methodology to provide better forecast information and services for different purposes, used effectively to build everything from global models
to models for man-made structures and people. The basic science andtechnology needed to handle big data is born from the research field of
data-centric science, which encompasses engineering for massive databases and the disciplines of statistical science, mathematical engineering, machine learning, and data mining. The Institute of Statistical Mathematics (ISM) is the only research institute in Japan to bring together
significant numbers of researches from each of these disciplines.
With the growing need for professionals in data-centric science, theInstitute launched the School of Statistical Thinking in fiscal 2011 as partof our project to foster statistical thinking, which is one of two major projects we are pursuing. The school accepts graduate students, researchers, and practitioners with deep knowledge of a particular specialized
discipline, and, with the cooperation of mathematics-related institutionsin and outside of Japan, strives to develop professionals who are highly
specialized while possessing the broad foundational knowledge necessary to put the fourth paradigm into action. We hope that the School of
Statistical Thinking will develop many next-generation librarians who
can play a curating role for science in the big data era, based on a thorough understanding of the trans-disciplinary qualities of statistical science linking a broad range of research disciplines.
Our other major project to establish Network Of Excellence (NOE) in
statistical mathematics has seen some changes. We closed our two strategic research centers in January 2012 and launched the Research Center
for Statistical Machine Learning and Service Science Research Center.
With this change, all of our research centers are now NOE-type centers,
establishing a framework for the pursuit of broad collaborative research
centering on five areas. While we are encouraging collaborative researchat the organizational level in order to strengthen the functions of today’s
inter-university research institutes, the project to establish NOE represents a trend-leading initiative for collaborative research.
Since the disaster in Japan last year, there has been renewed recognition of the importance of research that relates to massive and complex
systems, such as used in smart grid and other energy management andfor risk management of large-scale man-made structures. As a research
institute that carries out fundamental research for systems science, we
understand that we have a responsibility to meet society’s expectations.
We look forward to your continued understanding and support of ouractivities.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on the structural modeling of physical phenomenon related tonumerous factors, and it conducts research on model-based statistical inference methodologies. By means of
the modeling of spatially and/or temporally varying phenomena, complex systems, and latent structures, thedepartment aims to contribute to the development of cross-field modeling intelligence.

■ Spatial and Time Series Modeling Group —————————————————————————————————————The Spatial and Time Series Modeling Group works on the development and evaluation of statistical models, whichfunction effectively in terms of predicting phenomena or scientific discoveries, through data analysis and modeling related
to space-time-varying phenomena.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies in order to discover the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling.

■ Latent Structure Modeling Group ——————————————————————————————————————————The Latent Structure Modeling Group works on the modeling of variable factors as latent structures existing behindvarious dynamic phenomena in the real world. It also conducts research on methodologies for inference computation
associated with structures on the basis of data related to phenomena.

Department of Data Science
The aim of the Department of Data Science is to contribute to the development of natural and social sciences byconducting research into the methodology of designing statistical data collection systems, measuring and analyzing
complex phenomena for evidence-based sciences, and performing exploratory multivariate data analyses.

■ Data Design Group ——————————————————————————————————————————————————The Data Design Group focuses on research toward designing statistical data collection systems and developing therelated data analysis methods in a variety of research and experimental environments.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group studies methods for measuring and analyzing complex phenomena to extract statisticalevidence behind them in the various fields of science.

■ Structure Exploration Group —————————————————————————————————————————————
Structure Exploration Group advances statistical and mathematical research by applying or developing exploratory
multivariate data analyses to clarify latent structures of real phenomena in various fields of both natural and social sciences.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization, and algorithms in statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain
phenomena, stochastic processes and their application to inference, probability and distribution theory, and the related
mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data
mathematically and clarify the potential and the limitations of the data theoretically.

■ Computational Inference Group ———————————————————————————————————————————The Computational Inference Group studies mathematical methodologies in the research fields of numerical analysis,
optimization, discrete mathematics, and control and systems theory for computation-based statistical inference as wellas their applications.

2Strategic Research
Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the uncertainty and risks in society, which haveincreased with the growing globalization of society and the economy. The Center is also constructing a network for risk
analysis with the goal of contributing to creating a safe and resilient society.
Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict
the future and to produce designs for effective observation systems by means of “data assimilation”, which is afundamental technology integrating numerical simulations and observational data.
Survey Science Center
Founded on the accomplishments in social research by the Institute of Statistical Mathematics spanningover half a century including the Study of the Japanese National Character and the cross-national comparative
research on national characteristics, the Survey Science Center was established in January of 2011 in orderto facilitate further growth of the aforementioned sets of research as well as the establishment of networking
ties with both domestic and international research organizations and the increase in the capacity to makecontributions to wider society by creating what we call the NOE (Network Of Excellence).
Research Center for Statistical Machine Learning
Machine learning is a research field studying autonomous systems that learn from data. This field is based on
the statistical science that concerns inference from data and the computer science that studies algorithms. The
application of machine learning is broad, ranging from engineering, including robotics, and information sciences
to natural science, such as brain science. This research center aims at supporting the academic community of
the field, in addition to producing influential works via various joint projects.
Service Science Research Center
The aim of the newly established Service Science Research Center is to apply data-centric methodologies tothe service fields — from marketing, supply chain management, and management engineering, to the modelingof social systems. We will integrate the insights from these fields to establish data-centric service sciences as
a common discipline, through collaborations with universities and institutions worldwide, taking advantage of
ISM's Network Of Excellence (NOE) program.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

■ Computing Facilities Unit ———————————————————————————————————————————————The Computing Facilities Unit is in charge of managing computer facilities, software for research, networking infrastructureand network security.

■ Information Resources Unit —————————————————————————————————————————————The Information Resources Unit is responsible for maintaining an extensive library and an electronic repository, and is incharge of planning statistical education courses to popularize research results.

■ Media Development Unit ———————————————————————————————————————————————The Media Development Unit is in charge of the publication and editing of research results and is responsible for public relations.

3Research Centers
Risk Analysis Research Center

Suicide Prevention through Application of Spatio-temporal Analysis
and Factorial Design
N Small areal suicide data

Unfortunately, Japan is a country with one of thehighest suicide rates in the world. To address thisissue, small areal data, “Statistics of Community
for the Death from Suicide”, is being compiled. Thisis funded research from the National Institute of
Mental Health, at the National Center of Neurology
and Psychiatry in Japan. The data is divided in termsof area, year, age group, sex, manner of suicide, marital
status and occupation. To make certain charac­
teristics of the data recognizable, some visual­izations such as a choropleth map, a generalized
association plot (GAP), and RNavGraph (Fig. 1) havebeen applied.

data were used to detect the reason why people
are committing suicide. Marital status (especially
the widow rate), occupational status (especiallythe unemployment rate), and spatial autocorrelationof suicide rates were used to apply regression anal­
ysis. Metrological and geographical variables werealso used to detect territorial characteristics of sui­cide. Similarly, a structural equation model is appliedby adding financial variables. This analysis will beapplied to determine the relationship with mental
health, including medical factors.
Takafumi Kubota

N Detection of spatio-temporalclusters
To determine where and/or
when a highly concentrated area
and/or time is located, respective­
ly, spatio-temporal clustering is
first applied to the suicide data,be­cause the data possesses theadvantage of having areal andtemporal parameters. Figure 2
shows a choropleth map of thesuicide ratio (1988 - 1992) withspatial clusters (circled) and cor­
responding eche­lon dendrogramin­cluded. Further­­more, focuseson the Kanto region, on fewer ar­eas of suicides, and on increases
or de­creases in suicide rates areapplied.

Figure 1: Choropleth map of suicide rates from 1988 to 1992 (left),also displaying a GAP (upper right) and an RnavGraph (lower right)

N Factorial design of suicide

data
Spatio-temporal suicide data
were linked to multiple variables
that are related to suicide back­grounds at the same spatial loca­
tions and the same temporal peri­ods. Application analyses of these

Figure 2: Choropleth Map of EBSMR of suicide (1988 - 1992) with spatial clusters(circled; left) and corresponding echelon dendrogram ( right )4

Genome Analysis Project
N Mission of the project

The project was established in January of 2012.
We try to contribute to understand risk based on
genome information by developing methodologies
to discover useful knowledge to resolve various
issues in our world, such as personalized medicine
and biodiversity, and methodologies to quantify
latent risk by using the knowledge. Our problems
include developing methodologies such as control
of multiple comparison and influence analyses tofind genes which are susceptible to diseases, drug
responses, and heterosis. They also include
analyses of biodiversity, pathogens evolution, andstructure of amino acids. Quantifying risk of
disclosure of genome information is also included.
A result of our project is explained below.
N P-value estimation for gene expression data

There are many studies using gene expression
data. A typical study is to find differentiallyexpressed genes. They have been detected using
p-values with appropriate test statistics. The
p-values can be estimated using permutation
samples. However, such p-value estimates may beinaccurate because the number of experiments isoften very small on gene expression data.
To improve the accuracy of the p-value esti­
mation, the p-value on a gene has been estimatedincorporating the permutation samples on the othergenes. The number of permutation samples re­markably increases because there are many genes(typically thousands of genes) on gene expression
data, and then we expect that the p-value estima­tion becomes more accurate. However, this may
not be true, because the null hypothesis holds forsome genes but does not hold for the other genes.
To overcome this problem, a cleverly-devised
test statistic was proposed. We can make thecorresponding null statistic and then estimate thep-value appropriately incorporating the permuta­tion samples on the other genes. This idea is very
interesting but very ad hoc. There remains the ques­tion that there may be a more powerful test thanthe cleverly-devised test.
N New method discovered by mathematicalinvariance
We have considered what conditions allow us toappropriately incorporate the permutation samples
on the other genes and then formalized the
problem by mathematical invariance. We havederived the optimal test under the conditions
obtained above. This test will be difficult to
discover ad hoc and is theoretically ensured tohave a higher power than the tests proposed in thepast. Furthermore, it was shown by simulation anddata analysis that the obtained method worked
remarkably better than the methods proposed in
the past.
Shuhei Mano, Hironori Fujisawa

Figure 1: P-value estimation by incorporating the gene expres­sion data on the other genes

Figure 2: P-value estimator. The upper formula is a standard one.
The lower formula is a devised one.

Figure 3: Gene expression differences between B6 and MSM (Mam­malian Genetics Laboratory, National Institute of Genetics)

5Research and Development Center for Data Assimilation

Developing Statistical Analysis System for Parallel Computing Environment
N Statistics and computer
As one of the important objectives of statistics is tohandle data and retrieve useful information from them,statistical software has been indispensable in statistics
since the digital computer first appeared. Recently,
statistical software is used not only for manipulating
data but also for describing statistical models and data
analysis techniques. Data assimilation is one of suchcomputational intensive statistical methods which
require powerful computer abilities essentially.

N Super computer and the statistical analysis

system R
ISM owns world-class powerful super computer
systems for statistical science. It is natural that heavy
R users hope to run R on our super computers forhandling large and complicated data analysis. Al­
though R was not originally designed for using parallel
computation ability which is realized on the current
super computers, many researchers have added newfunctions for parallel computing on R. We use andimprove GotoBLAS to perform parallel computation
for large matrix calculations on our super computers.
Figure 3 compares our GotoBLAS and standard BLAS
library. Due to the CPU hardware design, perfor­
mance of calculation inevitably becomes bad for par­ticular data sizes. We can see that the performance of
GotoBLAS has less affected by the size of data than
the standard library. We also try to use R on Grid
and/or Cloud computer environment. Figure 4 dem­
onstrates the configuration of our system on which
two or more super computers in the remote places
with different architectures can perform calculations
in parallel.
Junji Nakano

N Statistical analysis system
Computer hardware has developed continuously
and rapidly in these decades. Computer software also
should advance together with it. Our main researchinterests are on statistical software that can use mod­ern computer hardware efficiently and effectively,
especially by developing user interface, graphics,
language, network usage for complicated statisticalanalysis. Our experimental statistical analysis system
Jasp has equipped and tested some of such new
functions, and tries to be a next generation statisticalanalysis system (Figure 1).
Currently, one of popular and practical statisticalanalysis systems is R. R is easy to use, reliable and freemulti-platform sta­tistical soft­ware, andhas been developed and sup­ported byvolunteer statisticians in all over theworld. R is a complicated and large-scale
system, which includes main body forbasic statistical computation and graph­ics, several tools for supporting statisticalresearch such as re­cording documents
and analysis history, interface to external
programs written in different computer
Figure 1:
languages. Figure 2 is an example of theStatistical analysis system Jasp
snapshot of R on Windows. The graphics
here are drawn by using our graphics
library Jasplot that are develop­ed in Java
language.

Figure 3: Comparison between the standard BLAS and GotoBLAS

6

Figure 2:
Snapshot of R on Windows using Javagraphical library Jasplot

Figure 4: System configuration of parallel R on remote clusters
by using GridRPCResearch Centers

Data Assimilation for Reproducing Temporal Evolution ofa High-dimensional System
N Estimation of high-dimensional system state

In spite of the availability of a large amount of datafrom a variety of observations, we can not hopethat such data would provide the whole view of asystem in general. On the other hand, recent pro­
gress in computer technology in terms of compu­
tational speed, parallel processing, and data storage
allows us to perform numerical simulations at highspatial resolution with high accuracy producing
realistic pictures of various phenomena in various
fields. Data assimilation compensate the lack of ob­
servational data with a simulation model aiming at
reproducing the phenomena as it actually occurred.
Data assimilation is also regarded as a way tocompensate imperfection of the numerical simula­tion model with the observational data. In order toreproduce natural phenomena using numericalsimulation, the initial state must be given accu­
rately. Moreover, even if we could completely know
the initial state, it is yet difficult to reproduce thetrue scenario due to various approximations andassumptions underlying the simulation model. A
simulation model could thus produce various
scenarios according to initial conditions, approxi­
mations, or assumptions. The data assimilation
approach seeks the most likely scenario among
those scenarios on the basis of the available obser­
vations.
In the data assimilation approach, we consider aprobability density function of all the variables in asimulation model to allow the variety of scenar­ios.
Although it is challenging to deal with a joint prob­
ability density of such a large amount of variables,
we address this task with the following two ap­
proaches.
N Estimation with enormous sample size

Figure 1: A schematic picture of a data assimilation algorithm which use
a massively parallel computer effectively

A probability density function can be
represented by a large amount of samples.
The advantage of this approach is that it
can deal with general non-linear and nonGaussian problems. However, the numberof the samples required in this approach
exponentially increases as the number ofthe variables increases. We are develop­
ing techniques to efficiently perform
estimation by using a massive­ly parallel
computer.
N Estimation with small sample size

Figure 2: An example of data assimilation with small sample size. The upper panels
show extreme ultraviolet (EUV) images from the IMAGE satellite. The lower pan­els show spatial distribution of plasma density estimated from the EUV data.

Data assimilation often deals with morethan millions (or even billions) of statevariables. A practical limited number ofrandom samples would not provide anappropriate approximation of a joint
probability density function of such anenormous number of variables. We are
studying efficient algorithms in whichcovariance matrices are approximated bya small number of selected samples andapplying such algorithms to various
domains.
Shin’ya Nakano
7Survey Science Center

Research on Survey Methodology and Planning of Collaborative
Training Survey
N Process of social survey and research on

survey methodology
The Institute of Statistical Mathematics (ISM) has
been conducting many social surveys for almost sixty
years. One purpose of these surveys is to study public
opinions in Japan. Another important purpose is to
study survey methodology in real-life settings.
Social survey mainly consists of two processes, as isdepicted in Figure 1. One is the process of sampling(right hand side), where representativeness of thesample is the main concern. The other is the process
of measurement (left hand side), where we try tocapture the opinion of respondents exactly. Eachstep in Figure 1 has its own methodological subjects
to be studied.

is a cooperative survey project between ISM and
NINJAL since 1950, and the survey in Tsuruoka-city,
Yamagata Prefecture, has been repeated at about
twenty-year intervals. The design of this survey is acombination of a repeated cross-sectional survey andpanel survey as is depicted in Figure 2.
We are planning to continue this kind of survey
practice for the coming years, with the support ofvisiting professors from outside the institute. The goalof this activity is to broaden the base of research on
survey methodology by fostering more survey pro­fessionals.
Tadahiko Maeda

N Practice of survey research by ISM
Formerly surveys of ISM were administeredby ourselves with the support from many
universities, employing students as part-time
interviewers. But during the past two decades
this tradition has been lost, entrusting most of
the survey operation to survey company.
Worsening survey circumstances in thepresent society do not allow us to ask nonprofessional interviewers to complete oursurvey. But this also meant that we have
gradually lost oppor­tunities for research on
survey methodology.
N Planning of the collaborative training

surveyMore recently, social science departments ofuniversities have undergone curriculum
reformation which places more emphasis on
survey practice. In line with this trend, Survey
Science Center is going to administer what wecall “Collaborative Training Survey”, underclose cooperation with university departments
and other survey-related institutions. Thus, wewill offer faculty members and students morechances to participate in large-scale surveys.
As a start-up, we conducted a nationwide
survey on “Social Stratification and Social
Psychology” (SSP survey) in 2010 with Osaka
University. In 2011 we carried out a joint survey
with National Institute for Japanese Language
and Linguistics (NINJAL) called “Fourth Surveyof Language Standardization in Tsuruoka”. This
8

Figure 1: Process of social survey and subject of research on survey
methodology

Figure 2: Design of Survey of Language Standardization in Tsuruoka,
Sixty years of Cooperative Project with NINJAL.
“Random” refers to a survey on random sample, “Panel”
refers to a panel survey on the same informants. Numbers in
the figure indicate sample size (as of Feb., 2012).

Research Centers

Multi-level Approaches in the Japanese National Character andOrganizational Climate
N The social system and the Japanese National

Character
The Institute of Statistical Mathematics has been
conducting a longitudinal nationwide survey, called“Nihonjin no Kokuminsei Chosa” on the Japanesenational character since 1953. The main goal of thesurvey is to investigate nature of Japanese people
and track change of Japanese society. Next survey,
which is 13th survey is planned to be conducted in
2013.
National character is seldom changed by exter­
nal change, while some institutional systems andcustoms are often changed by external reasons,such as rapid globalization in recent years. Does
this social system change impact on individual’s be­
haviors and attitudes? The answer is Yes. However,
ever since social system in Europe and the UnitedStates has been deployed in Japan, those systemchanges have not been fully penetrated into theJapanese social system. The difficulty of full deploy­ment is attributed to difference in each national
character. Therefore, it is important to clar­ify what
aspects of people’s ways of thinking have been chang­ed, which leads change of attitude trends in Japan.
N Moderating effect of organizational climate

In order to assess the most recent attitudes andbehaviors of the Japanese National Character inorganizational climate, it is important to examine
individuals embedded within organizations orcountries. To achieve this goal, on top of individual
assessment, we also focus on the variables in an
organization level for each organization, such as
corporate, school, regional society, etc. that an indi­vidual belongs to. Furthermore, we are planningcross-cultural research on the national character
including international comparison, so as to catch
the Japanese National Character in the wide range
and various contexts.
N Large-scale surveys and new statistics

Figure 1: The impact of NPM on intrinsic motivation

techniques
In order to develop a study of the Japanese
National Character, it is required to conduct
large-scale surveys based on a statistical sampl­
ing method, and carry out surveys on Japanese
National Character including international as wellas inter-organization comparative study by using
the new statistical techniques such as structur­
al equation modeling and multilevel analysis.
Yoosung Park

Figure 2: Movement to reconsider interpersonal relationships at theworkplace

Figure 3: Moderating effect of nation and organization in theJapanese National Character9Research Center for Statistical Machine Learning

Sparsity Based Information Processing
N Outline

In statistics, a method called LASSO was proposed
in 1990’s. The method employs the idea of sparsity,that is, a multi-dimensional information source isassumed to have a lot of zero components. Whenthe assumption is correct, it has been proved theo­
retically that the original source can be recon­
structed from restricted number of observations.
The sparsity based information processing becomes
an important research area not only in statistics, butalso in information theory, machine learning, and etc.
Those methods have been applied to many
types of data, such as genome data, image
data, and data in neuroscience. We are also
working for such applications, two of them areshown below.

The diffraction image corresponds to the power
spectrum of the electron density. The power spec­
trum is computed from the Fourier transform of theelectron density where only the power of each fre­quency component is given. In order to reconstruct
the electron density, we need the phase informa­tion. We have collaborated with Dr. Hidetoshi Kono
at the Japan Atomic Energy Agency, and have shownthat the sparsity based analysis can solve the phaseretrieval problem effectively.
Shiro Ikeda

N Sparse representation of motor control

First topic is neuroscience. In 1990’s, it has
been shown that the sparsity is important forvisual information processing in the brain. The
idea is widely accepted these days. However,visual information processing is a passive
process, and we do not know if the sparsity isimportant for active information processingin the brain.
We have collaborated with professor Yutaka
Sakaguchi at the University of Electro-Commu­
nications, and have studied the possibility ofsparse information representation for motor
control of two joints arm (Fig.1). We haveshown that a sparsity based model works wellfor motor control, too.

Figure 1: Results of proposed method

N Phase retrieval from X-ray diffraction

imaging
Another topic is optics. In RIKEN SPring-8
center at Harima, Japan, a new laser facility
called the X-ray free electron laser (XFEL) is
built, and new projects started. A project isabout the electron density estimation of a bio­
molecule from diffraction imaging. Commonlyused approach is to crystallize biomolecules,
and measure the diffraction image, but thegoal of the project is to use a single biomolecule
without crystallization.
Figure 2: SPR results with different μ for true diffraction data10

Research Centers

Statistical Natural Language Processing
N Overview

Natural language processing deals with humanlanguages to make computers understand andprocess them easily. Since the World Wide Web isintroduced in 1990s and electronic texts became
ubiquitous, the field has had deeper connections
with statistics to enhance the field theoretically. Now
statistical NLP is prevalent in many information
infrastructure in the society, and become one of theleading areas in machine learning.
While statistical NLP so far has focused on“syntactic” problems of language such as parsing,dependency analysis and morphological analysis,“semantic” aspects of language is almost inevitable
to explore when we consider the language as a meansof communication. Because there are no ex­plicit
annotation of meanings in the text, from a statisti­
cal point of view we have to regard them as latentvariables to estimate their values from large corpus
of electronic texts.
This project aims to enhance these novel
attempts of statistical NLP with the help of heritages
in statistics in order to propose deeper and moreadvanced statistical models of natural language.
N Bayesian statistics and natural language

Especially, opposed to the data in ordinary statis­
tics, natural language data are inherently discrete,sparse and very high-dimensional (lexicon usually
has more than tens of thousands of dimensions). In
order to deal with such sparse high-dimensionaldata, Bayesian statistics is so useful a tool that we
can also model “characteristics” of each text througha hierarchical Bayesian models.
Figure 1 shows a result of probabilistic latentseman­tic analysis, called LDA, on the beginning of
the novel “Yukiguni” of Yasunari Kawabata. Basedon these unsupervised analysis of language, we arealso able to identify when the semantic context
changes in a completely statistical fashion. Figure 2
is such a result, showing when the context change
occurs by tracking a latent distribution in the latentsemantic space by a sequential Monte Carlo method.

stamped texts, such as emails and twitters, andannual observations of language statistics, call forcontinuous-time models and time series analysis
methods. Figure 3 shows latent “image” of somemovies leaned from its reviews using Gaussian
processes. While this study is still preliminary, weare going to model and visualize such latent contin­uous events as emotions.
Associated with these research, we are now
conducting joint research with NTT Communi­
cation Science Laboratories, National Institute ofInformatics, and Advanced Industrial Science andTechnology. Furthermore, we started to cooperatewith the neighboring institute, National Institute ofJapanese Language, for statistical analysis of thebalanced corpus of contemporary written Japan­ese (BCCWJ).
Daichi Mochihashi

Figure 1: Probabilistic latent semantic analysis of the beginningof "Yukiguni", where colors indicate semantic topics

Figure 2: Semantic change point probabilities of a text throughprobabilistic latent semantic analysis

N Natural language and continuous space

Although language is discrete, behind them
there are hidden continuous space where meanings
and emotions reside. Furthermore, analyzing time-

Figure 3: Estimated “image” of movies from the review sentences,using Gaussian processes

11Service Science Research Center

New Statistical Methods for Service Design Science
N New framework for experimental studies in

N Elucidation of service process mechanism

service science
Design of Experiments (DOE), which was pro­
posed by Sir Ronald Fisher around 1920, is wellknown as one of powerful statistical methods forexperimental data. In addition, Quality Engineering(QE), which was developed by Dr. Genichi Taguchi,
has not only been recognized as one of effective
experimental data analysis in product design
science that elucidates the essence of “Mono­zukuri”, but is also utilized in many fields otherthan engineering. However, the aim of both DOE
and QE is implicitly to evaluate factorial effects
based on a small sample. On the other hand, unlike
product design science that focuses on uniform
quality value, service science deals with customers’
sense of values. Thus, under situations where
many different items must be measured in order toquantify customer behaviors and it is difficult toeffectively leverage the advantages of the DOE, it isnecessary for service science to provide effective
methods in order to evaluate the effects of service
strategies on customers’ behaviors using hugeamounts of time series data. We are constructing a
new framework for experimental studies in service
science.

based on observed data with application toservice strategies
Service industries, which have shifted from lowmix/high-volume production to high-mix/lowvolume production, are required to establish new
business models that are based not on corporate
ethics but on customer ethics. In order to develop
the business models, we have to consider customer
characteristics, environments, and behaviors that
may change on a daily life. To efficiently provide
services based on customer requirements, it isnecessary for the service providers to analyze
customer behaviors from various perspectives aswell as to extract potential customer character­istics and requirements based on observed data,
and to shed light on the mechanism for service
provision processes. We are conducting researchon the unification of inductive inference based ondata analysis methods and deductive inference
based on marketing theory, in order to create a newparadigm that links the combined inference to a newservice strategy based on customer perspectives.
Manabu Kuroki

Figure 2: A case study of service science(Provided by Prof. Sato, University of Tsukuba)
Figure 1: The combination of inductive and deductive inference
in service science(Provided by Prof. Sato, University of Tsukuba)

12Research Centers

Statistical Methodology Development for Robust Parameter Design
N Robust parameter design

Robust parameter design is a systematic engi­
neering methodology for quality improvement, andwas developed by Dr. Genichi Taguchi over theperiod of half a century. The purpose of this meth­odology is to find a good design of system control
factors to reduce variation of the system output
characteristic and functionality in order to makethe system robust against noise factors such asenvironmental use conditions. This methodology
calibrates system control factors in order to reduce
output variation in a cost-efficient approach. Thisis the core of design and development processes ofJapanese manufacturing industries.
N Optimal design of a push button switch

A push-button switch design is supposed to givea gentle but quick snapping motion when theswitch is pushed. This is caused by the reaction
force of the push-button switch when the skirt
portion is pushed and distorted. The deflection of
the switch skirt increases proportionally with thereaction force. However, when the reaction force
reaches a certain threshold (maximum) value, theskirt is deformed and the upper part of the button
reaches the bottom quickly to provide the On-Off
function. At the same time, the reaction force ofthe push-button switch is reduced dramaticallyfrom its maximum value to a minimum value.
The objectives of the optimization of the pushbutton switch are twofold: 1) stabilize the fun­ctionality of the switch against noise factors, and2) make the range between the maximum and mini­
mum values of reaction force as wide as possible.

The first objective is to find the most significant
factors that stabilize the functionality of the pushbutton switch, which is influenced by numerous
noise factors. The second optimization objective is
to maximize the range between maximum and mini­
mum reaction force to provide the user an appro­
priate feel for the switch action, which is designed
structurally to meet customer needs.
This research is based on the assumption of a
cubic polynomial model that describes the maxi­
mum and minimum values as well as the deflection
point for the input-output functionality of the pushbutton switch. The collected data are used toconstruct response function modeling (RFM) to
describe the behavior of the push-button switch.
The analysis results illustrate the comparison
between the initial design (Figure 1) and optimal
design (Figure 2). The results show the optimal
design has less variation (due to noise factors)
than the initial design, while its range between
maximum and minimum reaction forces is wider
than the range in the initial design.
Toshihiko Kawamura

Figure 1: Prediction graphics of the initial design

The maximum value
from 6.5 to be 8.0.

Figure 2 (a): Input-output response of the optimal design of
Scenario (a)Maximize the difference
between the maximum
and minimum values.

Figure 2 (b): Input-output response of the optimal design of
Scenario (b)13NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
N Biaxial Structure
The Institute of Statistical Mathematics pursues research and education along the two lines of basic research and strategic research, conducted by the basic
research departments along a horizontal axis and thestrategic research centers along a vertical axis (Figure1). By its nature, the basic research departments cuts
across and links various disciplines, developing tools
for interdisciplinary research. The field of statisticalmathematics must itself evolve to meet the changing
needs of society and the data environment, and is therefore constantly evolving as a field of study. At the sametime, there are approaches and directions that remainunchanged as the field evolves. For that reason, we
have chosen to call it “basic research”, to reflect boththe fixed and evolving qualities of statistical mathematics. There are three basic research departments:
statistical modeling, data science, and mathematicalanalysis and statistical inference. These departments
engage in cutting-edge research to develop methodologies for rational prediction and decision making,based on data and existing knowledge. Each of the Institute’s permanent researchers is assigned to one ofthese basic research departments.

N Research Facilities
The Institute’s research facilities comprise the strategic research centers and the school for developing
professionals. The strategic research centers (verticalaxis) conduct research activties that interface statistical mathematics with individual scientific disciplines,
in order to find solutions to urgent social problems.
These centers are staffed by permanent researchers
within the Institute, project researchers (post-doctoral
staff), and visiting professors and researchers. In

Biaxial
Structure

Professional
Development

School of Statistical Thinking

Service Science

Statistical Machine Learning

Survey Science

Mathematical
Analysis andStatistical Inference

Data Assimilation

Data Science( Top-down )Risk Analysis(Bottom-up )Basic Research

Statistical
Modeling

Strategic Research

Figure 1: Biaxial Structure of Basic Research and Strategic Re­
search (the Centers) and Professional Development in
the Institute of Statistical Mathematics.

14

January 2012, we closed the Prediction and Knowledge
Discovery Research Center and Research Innovation
Center. We then established the Research Center for
Statistical Machine Learning and Service Science Research Center. The two new centers join the existing
Risk Analysis Research Center, Research and Development Center for Data Assimilation, and Survey Science
Center. Additionally, the Institute launched School of
Statistical Thinking in November 2011 to foster and promote statistical thinking.

N NOE ( Network Of Excellence) ProjectIn accordance with the second medium-term plan of
the Research Organization of Information and Systems,
the Institute of Statistical Mathematics has set as a goal
the establishment of NOE (Network Of Excellence) in
statistical mathematics. We have been establishing
NOE in the five research areas of integrated risk research, next-generation simulation, survey science,statistical machine learning, and service science. NOE
pursues activities to establish new methodologies intheir respective research fields and serves as hubs forinterdisciplinary interaction. We operate NOE Promotion Unit to strengthen our coordination skills and tosupport the overall advancement of the NOE Project.
Advisory Board of NOE Project, which is organized byexperts in the private sector, academia, and the government (Table 1), advises our Managing Committee of
NOE Project and each strategic research center so that
we can formulate a unified management strategy tofacilitate the project for the crucial goal of erecting afourth scientific methodology in our knowledge society that extends beyond resolving individual problems.
The Institute has created a new logo in light of thecontinued growth of research under NOE. The logo

Senior Advisor, RIKEN Brain Science
Institute

Dr. Shun-ichi Amari

Adjunct Professor, Graduate Schoolof System Information, Kobe
University

Dr. Yoshio Oyanagi

Professor Emeritus, Tokyo University

Dr. Takamitsu Sawa
Dr. Motoyuki Suzuki

President, Center for International
Public Policy Studies

Prof. Naoki Tanaka

President, The Japan Pharmaceu­tical
Manufactures Association

Dr. Isao Teshirogi

Professor, Sociology and Research
Methodology School of Cultural andCreative Studies, Aoyama Gakuin University

Dr. Kazufumi Manabe

Director-General, Center for Research
Development and Strategy, Japan Scienceand Technology Agency / Highest Emeritus
Advisor, The National Institute of Advanced
Industrial Science and Technology

Dr. Hiroyuki Yoshikawa

Director-General, Institute for
Monetary and Economic Studies

Prof. Tomoo Yoshida

President, Shiga University

Table 1: Advisory Board of NOE Projectnew center includes researchers in fields bordering
service science, from within the Research and Development Center for Data Assimilation and the Institute.
In addition, the existing Risk Analysis ResearchCenter has shifted from research groups to a projectbased approach to pursue a broader scope of research
and flexibly adapt to changes in the research environment. By the abovementioned changes, all five of ourresearch centers are now NOE-type centers, to enableinterdisciplinary knowledge transfer revolving aroundthe Institute of Statistical Mathematics.

(Figure 2) symbolizes the advancement of comprehensive research through the combined and cumulative
efforts of the five NOE, alongside research efforts intheir respective research areas. The logo is used forthe activities of the NOE Project at the Institute and at
our related organizations.

N Establishment of New Research Centers
The Research Center for Statistical Machine Learning is one of two new research centers that we established in January 2012. The center was formed by combining the Statistical Genome Diversity ResearchGroup from the Prediction and Knowledge Discovery
Research Center, and the Functional Analytic Inference Research Group, Optimization-based Inference
Research Group, and Speech and Music Information
Research Group from the Research Innovation Center.
The new center includes researchers in other related
disciplines, and all researchers pursue broad and international research in collaboration with researchers
outside of Japan.
We also established the Service Science ResearchCenter, which was formed with the Research Group for
Reliability and Quality Assurance of Service and Productfrom the Risk Analysis Research Center as its base. This

N Expansion of Activities by NOE
The Institute is actively pursuing agreements withother research organizations, in order to expand therole of the five research centers as core research hubs.
In the 2011–2012 academic year, we have signed new
Memorandum of Agreements with one Japanese institute and three foreign organizations.
From a methodological perspective, our Institute is
focusing all activities on advancing research in eachof the research fields of risk research, next-generation
simulation, survey science, statistical machine learning, and service science. And we are going to unite thefive NOE mentioned above into one as the NOE Research Promotion Organization. Using this, the Institute will aim to develop new research areas and establish new collaborative research systems (Figure 3). To
learn more about the project, please visit the websiteof the NOE Project (http://noe.ism.ac.jp/).

Figure 2: logo

Advice from
Advisory Board

New Relatedorganizations signed
MOA in 2011-2012

Jul. 2008

Graduate School of Life Science,
Tohoku Univ.

Future related
research organizations

Mar. 2011

Next-Generation
Simulation NOE

Risk Analysis
Research Center

Service Science
NOE

The Institute of Statistical
Mathematics
Research andService Science

Research Center

Dept. of Empirical Inference of Max-Planck
Institute for Biological Cybernetics(Germany) / Aug. 2010

The Human Language Technology Dept.,
Institute of Infocomm Research(Singapore)(Germany) //Aug.
Jan. 20102012

Center for Computational Statistical Machine
Learning, University College London

Advisory Board
of NOE Project

Feb. 2007

Institute of Fluid Science,
Tohoku Univ.
Jul. 2010

Solar-Terrestrial Environment
Laboratory, Nagoya Univ.
Sep. 2010

Future related
research organizations

NOE Research Promotion
Organization Survey Science

Research Center
for Statistical
Machine Learning

CenterFuture related
research organizations

Future related
research organizations

Graduate School of Human Science,
Osaka Univ.

(UK) / Feb. 2012

Dept. of Communication Systems, SINTEF
Information and Communication Technology

RIKEN Computational Science
Research Program

Development Center
for Data Assimilation

Managing Committeeof NOE Project

NOE Promotion
Unit(Norway) / Jan. 2012

Related researchorganizations

42 organizationsin Risk Research Network

Dept. of Risk Engineering, Graduate
School of Systems and Information
Engineering, Tsukuba Univ.

Related by MOA

Future related
research organizations

Centers whose roleis the “core” of NOE

Risk Research NOE

Organization
Cooperation in ISM

Jul. 2010

Statistical Machine Learning NOE

National Institute for Japanese
Language and Linguistics
Oct. 2012

Survey Science NOE

Figure 3: Relationship Diagram of NOE ( Network Of Excellence )15

Project for Fostering and Promoting Statistical Thinking

Because of the “data explosion” in modern society, people who can think and handle such data statistically and retrieve useful information are greatly needed in all fields. Unfortunately, the Japanese
education system has fallen short in producing a sufficient number of people who have useful statisticalthinking abilities. We established the School of Statistical Thinking in ISM to improve this situation. We
hope that our activities, including tutorial courses, seminars, and cooperative research projects, areuseful for developing professionals with the proper knowledge and skills to tackle complicated problems
involving huge amounts of data.

Research Collaboration Start-up
The Institute has been providing a consulta­tion service on statistical science. Along with the launchof the School of Statistical Thinking in November 2011, this service was reorganized as a research collaboration start-up. This program, being one of the projects to foster and promote statistical thinking, ismainly aimed at supporting applied scientists. Expert statisticians affiliated with the Institute give scientists advice on statistical modeling and data analysis. Some cases develop to the point of official researchcooperation, which is our primary duty as an interuniversity research institute. The start-up program
covers a variety of topics ranging from introductory to more specialized studies. Half of the clients arefrom the private sector, and the rest are staff at public organizations, university professors, or students.
The Institute accepts about 20 cases annually, some of which benefit society in diverse ways.

Open-type Human Resource Development Program
In order to foster and promote “statistical thinking”, the Institute of Statistical Mathematics is calling
for research meeting and other workshop applications, particularly those related for developing young
human resources.
Four workshops have already been selected in FY 2012.

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on
statistical mathematics every Wednesday. The
seminars are led by in-house and external lecturers to showcase their latest studies. These lectures
are free to attend. To view the seminar scheduleand learn more about the program, please visit theInstitute of Statistical Mathematics website.
http://www.ism.ac.jp/

16Open Lecture
We hold an open lecture during Education and Culture Week every year (November 1 through 7),
to introduce the Institute’s activities and to promote statistical science. We invite lecturers to speak
on a timely topic relating to statistical science. The lecture is open to the general public. For further
information, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/

Tutorial Courses■ History
The Statistical Education Program started in 1944, the year that the Institute of Statistical Mathematics was founded, as an education program at the Numerical Computation Training Center of the Science
Research Technical Assistant Training Center of the Ministry of Education, located within the Institute.
In 1947, the affiliated Statistical Technician Training Center was opened as an educational organization
for statistical technicians and instructors.
As social needs have changed, the purpose of the education program has gradually shifted away fromsupplying statistical technicians for the government, towards educating working people. Tutorial courses were therefore initiated.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation, and the affiliated Statistical Technician Training Center was abolished. However, in response toconsistent public demand for tutorial courses, the Center for Engineering and Technical Support, together with other departments, ran three to four courses annually. In 2005, the number of courses rose
to 13. In 2011, the number of courses was 10.

■ Courses
The total number of courses held from 1969 to March, 2012 was 282, with a total of 20,687 participants.
These courses covered a wide range of fields from basic to applied statistics. The following table lists thecourses held in the past year:
Year

Category

Title

Month

2011

Basic course

Introduction to Sampling Methods and Sample Surveys

May

39

Basic course

Akaike Information Criterion and Statistical Modeling

June

92

Basic course

Introduction to Multivariate Analysis

August September

94

Advanced course

New Development of Model-Free Controller Design
September— Basics and Applications of Fictitious Reference Iterative Tuning (FRIT ) —18

Basic course

Basic Course of Statistics

October

85

Standard course

Statistical Pattern Recognition —Toward Comprehensive Understanding— October

93

Advanced course

An Introduction to Statistical Analysis Based on Martingale Theory

November

53

Standard course

Theory and Practice for Inferring Molecular Phylogenies

November

35

Advanced course

Implementation of the Ensemble Kalman Filter

December81

Basic course

Introduction to Time Series Analysis for Bioscience

March

91

2012

Number of participants

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html
17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washington)

July 27, 1988 -Stichting Mathematisch Centrum

The Kingdom of theNetherlands (Amsterdam )May 10, 1989 -Statistical Research Center for Complex Systems, Seoul National University

The Republic of Korea (Seoul )Germany (Berlin)

October 17, 2002 June 30, 2005 -The Steklov Mathematical InstituteTaiwan ( Taipei )Russia (Moscow)

Central South University

China (Changsha)

November 18, 2005 -Soongsil University

The Republic of Korea (Seoul ) April 27, 2006 The United Kingdom (Coventry) January 16, 2007 India (Kolkata )October 11, 2007 -Institute for Statistics and Econometrics, Humboldt University of Berlin
Institute of Statistical Science, Academia Sinica

Department of Statistics, University of Warwick
The Indian Statistical InstituteDepartment of Empirical Inference, Max Planck Institute for Biological Cybernetics
Faculty of Medicine, University of São Paulo

Conclusion day

Germany ( Tubingen)
Brasil (São Paulo)

December 8, 2004 August 9, 2005 -August 11, 2010 April 15, 2011-

Norway ( Trondheim)
Singapore (Singapore)

January 30, 2012-

Human Language Technology Department Institute for Infocomm ResearchCentre for Computational Statistics and Machine Learning, University College London

The United Kingdom (London)February 16, 2012-

Department of Communication Systems, SINTEF Information and Communication Technology

February 16, 2012-

Research Collaboration
This academic study program provides research­ers from other academic institutes with access to thefacilities of the Institute, and provides opportunities for researchers to conduct theoretical and appliedstudies on statistics.

■ Number of Activities
2006

2007

2008

2009

2010

2011

122

120

138

154

135

172

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below tofind the most appropriate type of project.
ISM Fields
Numberabcde

FieldsNumberSpatial and time series modelingIntelligent information processing
Graph modeling
Survey researchMultidimensional data analysis

f
g
h
i
j

FieldsComputational statistics
Mathematical statistics
Learning and inference
Computational mathematics
OthersMajor Research Fields
Number

1
2
3
4
5
6
7
8

18

FieldsMajor Research Domains

Statistical mathematics
Information science
Biological science
Physical science
Engineering
Human science
Social science
OthersMathematical theory of statistics, optimization, etc.
Algorithms, use of computer in statistics, etc.
Medicine, pharmacy, epidemiology, genetics, etc.
Space, planet, earth, polar region, materials, etc.
Mechanics, electronics, control, chemistry, architecture, etc.
Philosophy, art, psychology, education, history, geography, culture, language, etc.
Economics, law, politics, society, management, official statistics, population, etc.
Other research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education
and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

President

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The course includes designing of data-gathering systems, modeling, inference and forecasting in order to extract information and knowledge from the real world based on empirical data, as well as basic,mathematical and applied education and research related to these fields. The course aims to provide thestudent with skills that help to contribute to solving important and connected issues and give the ability
to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

19

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science byprofessors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of

the best in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible

for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted● Requirements to complete the course are as follows:

Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research

results.

Number of Students ( As of April 1, 2012)■ Doctor’s course five years■ Doctor’s course three years

Year of enrollment

2008

2009

2010

2012

Year of enrollment

2004

2007

2008

2009

2010

2011

2012

Number of students2

1 (1 )

2

2

Number of students

1

2

3

3

5

8

2

* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University (1 ) • Tohoku University ( 2 ) • Fukushima University (1 ) • University of Tsukuba (6) • Saitama University (1 )• Ochanomizu University (1 ) • Hitotsubashi University ( 6 ) • Chiba University (1 ) • The University of Tokyo (16 ) • Tokyo Medical
and Dental University (1 ) • Tokyo Gakugei University (1 ) • Tokyo Institute of Technology ( 4 ) • Tokyo University of Marine
Science and Technology (1 ) • Tokyo University of Agriculture and Technology (1 ) • Tokyo Metropolitan University (1 ) • Shizuoka
University (1 ) • Japan Advanced Institute of Science and Technology, Hokuriku (1 ) • Nagoya University ( 3 ) • Toyohashi Universityof Technology ( 2 ) • Kyoto University ( 4 ) • Osaka University ( 2 ) • Osaka City University (1 ) • Okayama University ( 2 ) • Shimane
University ( 3 ) • Kyushu University ( 2 ) • Oita University (1 )20Graduate School Program

University Background of Students
Private universities• Okayama University of Science (1 ) • Tokyo University of Science ( 5 ) • Kyoto Sangyo University (1 ) • Keio University ( 5 )• Waseda University ( 6 ) • Chuo University ( 7 ) • Toyo University (1 ) • Nihon University ( 2 ) • Hosei University ( 7 ) • Kurume
University (1 ) • Japan Women’s University (1 ) • Shibaura Institute of Technology (1 ) • Nanzan University(1)Foreign universities• Aston University (1 ) • University of California, Irvine (1 ) • Universidade Eatadual of Campians (1 ) • University of Colorado at
Boulder ( 2 ) • University of Dhaka ( 2 ) • University of Hawaii (1 ) • Jahangirnagar University ( 2 ) • University of Malaya (1 ) • Ohio
University (1 ) • University of Rahshahi ( 2 ) • Stanford University (1 ) • Center for Analysis and Prediction, China Seismological
Bureau (1 ) • Northeastern University of Technology, China (1 ) • The Hong Kong University of Science and Technology (1 )• University of Science and Technology of China (1 ) • Chinese Academy of Sciences, Institute of Applied Mathematics (1 )Degrees Awarded
Year

2006

2007

2008

Doctor of Philosophy

8 [ 1]

7 [ 1]

4 [ 1]

2009

2010

2011

5 [ 1]

7 [ 1]

4* [ ] Ph.D. on the basis of the dissertation only (included in the total)

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Instituteof Technology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Hokkaido University• Tokyo Institute of Technology • Hiroshima University • JAXA’s Engineering Digital Innovation Center • Kyoto University • Nara
Institute of Science and Technology • Bank of Japan • Japan Broadcasting Corporation • Railway Technical Research Institute• Statistical Information Institute for Consulting and Analysis • Government Pension Investment Fund • Public School
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Tokyo University of Information Science • Shibaura Institute of Technology • Rikkyo University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc.

21

Facilities and Equipment

Computation Resources
Since January 2010, the Supercomputer System
for Statistical Science has been in operation andhas analyzed a large volume of statistical data. The
main components are a shared memory system(Fujitsu SPARC Enterprise M9000 with 2 nodes, thefirst with 64 quad-core SPARC64 CPUs and 2 TB ofmain memory, and the second with 24 quad-core
SPARC64 CPUs and 1 TB of main memory), and a
distributed memory system (Fujitsu PRIMERGY
RX200S5 with 360 nodes, each with 2 quad-core
Xeon X5570 CPUs and 48 or 24 GB of main memory.
In total, 2880 cores and 12.1 TB of memory are available). The system also includes a large-scale sharedstorage system (1.37 PB disk storage supported by
RAID6), a physical random number generating system (two random number generator boards, eachof which can generate random numbers at 400MB/s) and a visualization system (including a
SXRD projector with a maximum resolution of 4,096
× 2,160 and a 200-inch rear projection screen).
In the office building, the primary Local Area Network (LAN) consists of an Ethernet network using
10GBASE-SR for the main trunk and 1000Base-T forbranches. The personal computers in researchers’
offices, the Supercomputer System for Statistical
Science are all connected to this network. A wire-

less LAN system that supports IEEE 802.11a,b,g,n
is also available in the area of the building occupied by the institute. These LAN systems enabledistributed processing and computing resources
and statistical data to be used effectively. Comprehensive network security measures have been implemented such as a firewall system, anti-virus
software, and an intrusion prevention system. To
encourage joint research with researchers both inJapan and abroad, as well as the exchange of emails, the network is connected to the Internet
through SINET3 (1 Gbps).

Supercomputer System for Statistical Science

Library and Materials ( As of April 1, 2012)
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that
we publish ourselves: Annals of the Institute of Sta­tistical Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics(Japanese), ISM Survey Research Report (Statistical Researches mainly related to the Japanese
National Character), Computer Science
Monographs, Cooperative Research Re­
ports (for collaborative research proJapanese Foreign
periodicals periodicals
jects), Research Memorandum, ISM Reports on Statistical Computing, and ISM
1,009
1,174
Report on Research and Education.
Periodicals 54%
46%
2,183
All materials are properly catalogued
and can be searched from the web inorder to meet the needs of research­ers
working in a wide of fields. We also accept photocopy requests.

We have a large number of major Japanese/foreign journals covering a wide variety of fields including statistics, mathematics, computer scienceand informatics. In addition, we also have a large
library consisting of books on humanities, social
science, biology, medical science, science and engineering.

Japanesebooks
16,57527%

Books
61,353Foreign books
44,77873%

22

Finance and Buildings

Administration Subsidy and Others ( 2011)Type

Personnel expenses

Non-personnel expenses

Total

765,005

881,427

1,646,432

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2011)
Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

9

4

4

17

Income46,044

5,566

5,993

57,603
Unit: ¥1,000

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2011)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research ( S )130,420Grant-in-Aid for Scientific Research ( A)3

31,850Grant-in-Aid for Scientific Research ( B)3

12,090Grant-in-Aid for Scientific Research ( C)15

15,129Grant-in-Aid for Challenging Exploratory Research

4

6,760

Grant-in-Aid for Young Scientists ( B )811,440Grant-in-Aid for Research Activity Start-up

1

1,222

Grant-in-Aid for JSPS Fellows

1

800

36

109,711

1

3,000

Total
Health and Labour Sciences Research Grants

Unit: ¥1,000

Site and Buildings ( As of April 1, 2012)
Site Area

62,450m 2

Area for Buildings (total)

16,026m 2

23

Organization

Organization Diagram ( As of October 1, 2012)■ Spatial and Time Series Modeling Group

Cooperative
Research
Committee

Department of Statistical Modeling■ Latent Structure Modeling Group■ Data Design Group

Department of Data Science■ Metric Science Group■ Structure Exploration GroupCouncil

Scientific
Advisory
Board■ Complex System Modeling Group

Department of Mathematical
Analysis and Statistical Inference■ Mathematical Statistics Group■ Learning and Inference Group■ Computational Inference Group

Risk Analysis Research Center
Research and Development
Center for Data Assimilation

DirectorGeneral

Survey Science Center
Research Center for
Statistical Machine Learning

Vice DirectorGeneral

Service Science ResearchCenterSchool of Statistical Thinking

Center for Engineering andTechnical Support■ Computing Facilities Unit
■ Information Resources Unit
■ Media Development Unit

Library

Planning Unit
Evaluation Unit
Administration Planningand Coordination Section

Information and Public Relations UnitIntellectual Property Unit
NOE Promotion Unit

National Institute ofPolar Research

Planning Section ( NIPR )NIPR / ISM
Joint Administration Office

The Institute of
Statistical Mathematics

Planning Section ( ISM )General Service Center24

Number of Staff ( As of April 1, 2012)
DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Professor Professor rative Staff
Staff

1

Total

1

Department of Statistical Modeling

5

8

2

15

Department of Data Science

6

7

6

19

Department of Mathematical Analysis and Statistical Inference

6

5

4

15

Center for Engineering and Technical Support

99Administration Planning and Coordination Section

1

NIPR/ISM Joint Administration Office

12 ( 29)

1 ( 2)

13 ( 31)13 ( 29)

10 ( 2 )

73 ( 31)

Total

1

17

20
(

12

1

) Total Number of Staff of NIPR/ISM Joint Administration Office

Staff ( As of October 1, 2012)
Director-General

Tomoyuki HIGUCHI

Vice Director-General

Hiroe TSUBAKI

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Hiroshi MARUYAMA

Department of Statistical Modeling
Director Tomoko MATSUI• Spatial and Time Series Modeling Group —————————————————————————————————————————————————————
Prof.

Nobuhisa KASHIWAGIProf.

Tomoyuki HIGUCHI

Assoc. Prof.

Genta UENO

Assist. Prof.

Shinya NAKANOAssoc. Prof.

Jiancang ZHUANG• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.

Yoshiyasu TAMURA

Prof.

Junji NAKANOAssoc. Prof.

Yukito IBA

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Assist. Prof.

Shinsuke KOYAMA• Latent Structure Modeling Group ——————————————————————————————————————————————————————————Prof.

Hiroshi MARUYAMAProf.

Tomoko MATSUIAssoc. Prof.

Seisho SATO

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Yoshinori KAWASAKI

Department of Data Science
Director Takashi NAKAMURA• Data Design Group ———————————————————————————————————————————————————————————————————
Prof.

Takashi NAKAMURA

Prof.

Ryozo YOSHINO

Assoc. Prof.

Naomasa MARUYAMA

Assoc. Prof.

Tadahiko MAEDA

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Toshihiko KAWAMURA

25

Department of Data Science• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Prof.

Shigeyuki MATSUIAssoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assist. Prof.

Nobuo SHIMIZU

Assist. Prof.

Hisashi NOMA

• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.

Hiroe TSUBAKI

Prof.

Koji KANEFUJI

Assoc. Prof.

Jun ADACHI

Assoc. Prof.

Manabu KUROKI

Assist. Prof.

Ying CAO

Assist. Prof.

Koken OZAKI

Assist. Prof.

Yoo Sung PARK

Department of Mathematical Analysis and Statistical Inference
Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Assoc. Prof.

Yoichi NISHIYAMA

Assoc. Prof.

Shuhei MANO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Shogo KATO

Assist. Prof.

Kei KOBAYASHI

Visiting Prof.

Yoshihiko KONNO• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Assoc. Prof.

Hironori FUJISAWA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shiro IKEDA• Computational Inference Group ———————————————————————————————————————————————————————————
Prof.

Yoshihiko MIYASATO

Assist. Prof.

Tadayoshi FUSHIKI

Prof.

Atsushi YOSHIMOTO

Prof.

Satoshi ITO

Risk Analysis Research Center
Director Hiroe TSUBAKI
Prof.

Hiroe TSUBAKI

Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Shigeyuki MATSUIProf.

Shinto EGUCHIProf.

Koji KANEFUJI

Prof.

Nobuhisa KASHIWAGI

Prof.

Atsushi YOSHIMOTO

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Manabu KUROKI

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Yoshinori KAWASAKI

Assoc. Prof.

Yoichi NISHIYAMA

Assoc. Prof.

Seisho SATO

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Hironori FUJISAWA

Assoc. Prof.

Jun ADACHIAssist. Prof.

Takaaki SHIMURA

Assist. Prof.

Ying CAO

Assist. Prof.

Shogo KATO

Assist. Prof.

Hisashi NOMA

Assist. Prof.

Tadayoshi FUSHIKI

Project Assoc. Prof. Takaki IWATA

Project Assist. Prof. Takafumi KUBOTA

26

Vice Director Satoshi YAMASHITA

Project Assist. Prof. Takayuki YAMADA

Visiting Prof.

Sadaaki MIYAMOTO
Tosiya SATO

Visiting Prof.

Rinya TAKAHASHI

Visiting Prof.

Manabu IWASAKI

Visiting Prof.

Visiting Prof.

Yoichi KATO

Visiting Prof.

Masaaki MATSUURA

Visiting Prof.

Kunio SHIMIZU

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Megu OTAKI

Visiting Prof.

Tetsuji IMANAKA

Visiting Prof.

Nobuo YOSHIDA

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Toshio HONDA

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Shinji TODA

Visiting Prof.

Hirohisa KISHINO

Visiting Prof.

Hidetoshi SHIMODAIRA

Visiting Prof.

Tatsuhiko TSUNODA

Organization

Staff
Risk Analysis Research Center
Visiting Assoc. Prof. Hideki KATAGIRIVisiting Assoc. Prof. Koji OKUHARA

Visiting Assoc. Prof. Toshikazu KITANO

Visiting Assoc. Prof. Hisayuki HARA

Visiting Assoc. Prof. Toshio ONISHI

Visiting Assoc. Prof. Satoshi TERAMUKAI

Visiting Assoc. Prof. Jun OHASHI

Visiting Assoc. Prof. Hisateru TACHIMORI

Visiting Assoc. Prof. Makoto TOMITA

Visiting Assoc. Prof. Toshihiro HORIGUCHI

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Assoc. Prof. Yoshiyuki NINOMIYA

Visiting Assoc. Prof. Satoru ENDO

Visiting Assoc. Prof. Hitoshi ISHIKAWA

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Toshiaki OWARI

Visiting Assoc. Prof. Masakazu ANDO

Visiting Assoc. Prof. Yasutaka SHIMIZU

Visiting Assoc. Prof. Masaaki FUKASAWA

Visiting Assoc. Prof. Takahiro YONEZAWA

Visiting Assoc. Prof. Hiroshi SYONO

Project Researcher

Project Researcher

Ryo AKAISHI

Project Researcher Takao KUMAZAWA

Chiho KAMIYAMA

Research and Development Center for Data Assimilation
Director Tomoyuki HIGUCHI

Vice Director Yoshiyasu TAMURA

Prof.

Tomoyuki HIGUCHIProf.

Yoshiyasu TAMURA

Prof.

Junji NAKANOAssoc. Prof.

Seisho SATO

Assoc. Prof.

Genta UENO

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Yukito IBA

Assist. Prof.

Shinya NAKANO

Project Assoc. Prof. Hiromichi NAGAO

Project Assist. Prof. Christopher Andrew ZAPART

Project Assist. Prof. Masaya SAITO

Visiting Prof.

Visiting Assoc. Prof. Toru ONODERA

Visiting Assoc. Prof. Koji HUKUSHIMA

Visiting Assoc. Prof. Kazuyuki NAKAMURA

Takashi WASHIO

Survey Science Center
Director Ryozo YOSHINOProf.
Assoc. Prof.

Ryozo YOSHINOProf.

Takashi NAKAMURA

Assoc. Prof.

Tadahiko MAEDA

Takahiro TSUCHIYA

Assist. Prof.

Koken OZAKI

Assist. Prof.

Yoo Sung PARK

Visiting Assoc. Prof. Toru KIKKAWA

Visiting Assoc. Prof. Takahito ABE

Visiting Assoc. Prof. Wataru MATSUMOTO

Project Researcher Kosuke NIKAIDO

Project Researcher Kiyohisa SHIBAI

Project Researcher Naoko KATO

Project Researcher

Taisuke FUJITA

Research Center for Statistical Machine Learning
Director Kenji FUKUMIZU

Vice Director Tomoko MATSUIProf.

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yosihiko MIYASATO

Prof.

Satoshi ITO

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Assist. Prof.

Tadayoshi FUSHIKI

Assist. Prof.

Kei KOBAYASHI

Assist. Prof.

Shinsuke KOYAMA

Visiting. Prof.

Atsuko IKEGAMI

Visiting. Prof.

Takashi TSUCHIYA

Visiting. Prof.

Tadashi WADAYAMA

Visiting. Prof.

Masataka GOTO

Visiting. Prof.

Koji TSUDA

Visiting Assoc. Prof. Yuji SHINANO

Visiting Assoc. Prof. Takafumi KANAMORIVisiting Assoc. Prof. Arthur GRETTON

Project Researcher Yu NISHIYAMA

27

Service Science Research Center
Director Hiroshi MARUYAMAProf.

Hiroshi MARUYAMAProf.

Tomoyuki HIGUCHIProf.

Hiroe TSUBAKI

Prof.

Tomoko MATSUIProf.

Junji NAKANOAssoc. Prof.

Manabu KUROKI

Assist. Prof.

Toshihiko KAWAMURA

Assist. Prof.

Nobuo SHIMIZU

Visiting Prof.

Yoichi MOTOMURA

Visiting Prof.

Shusaku TSUMOTO

Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Yoshiki YAMAGATA

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Tadahiko SATO

Visiting Assoc. Prof. Yukihiko OKADA

Project Researcher Hisanao TAKAHASHI

School of Statistical Thinking
Director Junji NAKANOProf.
Adjunct Professor

Vice Director Yoshinori KAWASAKI

Hiroshi MARUYAMA

Adjunct Professor

Makio ISHIGURO

Project Assist. Prof. Osamu KOMORI

Masami HASEGAWA

Adjunct Professor

Yasumasa BABA

Project Researchers
Project Assoc. Prof. Kazuhiro MINAMI

Project Assist. Prof. Kazuhiko SHIBUYA

Project Researcher Motoi OKAMOTO

Project Researcher Satoko SAITA

Project Researcher Xiaoling DOU

Project Researcher Eiki TANAKA

Project Researcher Kazue SUZUKI

Center for Engineering and Technical Support
Director
Deputy Manager

Junji NAKANO
Yuriko WATANABE

Head of Computing Facilities Unit

Kazuhiro NAKAMURA

Head of Media Development Unit

Yuriko WATANABE

Vice Director
Senior Specialist

Yoshinori KAWASAKI
Saeko TANAKA

Head of Information Resources Unit

Saeko TANAKA

Library
Head Junji NAKANO

Administration Planning and Coordination Section
Director Tomoyuki HIGUCHI28

Head of Planning Unit

Hiroe TSUBAKI

Head of Evaluation Unit

Yoshiyasu TAMURA

Head of Information and Public Relations Unit

Hiroshi MARUYAMAHead of Intellectual Property Unit

Hiroshi MARUYAMAHead of NOE Promotion Unit

Hiroe TSUBAKI

Visiting Prof.

Takashi NAMESHIDA

Organization

Staff
NIPR/ISM Joint Administration OfficeDirector

Tsugio TOKUTA

Director of General Service Center

Akira UCHIYAMA• Planning Section ( ISM ) —————————————————————————————————————————————————————————————————————
Head of Planning Section Kenichi TADA
Deputy HeadTeam Leader

Mitsuaki OGAWA

Specialist

Fumio SUTO

Mitsuo ENDOTeam Leader

Hiroaki ARAISpecialist

Tomonori SANO

• Planning Section ( NIPR) ————————————————————————————————————————————————————————————————————
Head of Planning Section Seiko TOBE
Deputy Head

Yasuyuki EDURETeam Leader

Kazuhisa OSHITATeam Leader

Michihito SAKURAI

Specialist

Hiroyasu KUMAGAITeam Leader

Norihito SETO

• General Service Center —————————————————————————————————————————————————————————————————————
Deputy Head

Takashi KOSAKA

Deputy Head

Koji SAKAMOTO

Deputy Head

Takashi KUBOTA

Deputy HeadTomohiko MIYAUCHITeam Leader

Kazuyoshi ASO

Team Leader

Yoji ISHIITeam Leader

Kenichi SHIOBARA

Specialist

Yoshihiro YAMADASpecialist

Yumiko OKAWA

Council of The Institute of Statistical Mathematics ( As of April 1, 2012 )Yasushi AKIYAMA

Professor, Graduate School of Information Science and Engineering , Tokyo Institute of Technology

Masanori IYEProfessor, Optical and Inferred Astronomy Division, National Astronomical Observatory of Japan, National Institutesof Natural Sciences

Koji KURIHARA

Professor, Okayama University Executive Vice Director / The Graduate School of Environmental and Life Science

Kunio SHIMIZU

Professor, Faculty of Science and Technology, Keio University

Nobuhiko TERUIProfessor, Graduate School of Economics and Management, Tohoku University

Ryuei NISHII

Professor, Institute of Mathematics for Industry, Kyushu University

Yoshihiro YAJIMA

Professor, Graduate School of Economics, The University of Tokyo

Shoichi YOKOYAMA

Professor, Department of Linguistic Theory and Structure, National Institute for Japanese Language and Linguistics

Takashi WASHIO

Professor, The Institute for Scientific and Industrial Research, Osaka University

Michiko WATANABE

Professor, The Graduate School of Health Management, Keio University

Hiroe TSUBAKI

Professor (Vice Director-General, ISM )Yoshiyasu TAMURA

Professor ( Vice Director-General, ISM )Hiroshi MARUYAMA

Professor ( Vice Director-General, ISM )Tomoko MATSUI

Professor (Director of Department of Statistical Modeling, ISM )Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM )Satoshi KURIKI

Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM )Junji NAKANOProfessor (Director of Center for Engineering and Technical Support, ISM )Shinto EGUCHI

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Kenji FUKUMIZU

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Nobuhisa KASHIWAGI

Professor (Department of Statistical Modeling, ISM )Ryozo YOSHINO

Professor (Department of Data Science, ISM )29

Organization

Cooperative Research Committee ( As of April 1, 2012)
Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Akifumi OIKAWA

Professor, The Graduate University for Advanced Studies

Toshimitsu HAMASAKI

Associate Professor, Graduate School of Medicine, Osaka University

Yoshinori FUJII

Professor, Faculty of Education and Culture, University of Miyazaki

Yuichi MORI

Professor, Faculty of Informatics, Okayama University of Science

Tomoko MATSUI

Professor (Director of Department of Statistical Modeling, ISM )Ryozo YOSHINO

Professor (Department of Data Science, ISM )Yoshihiko MIYASATO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Atsushi YOSHIMOTO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Research Ethics Review Committee ( As of April 1, 2012)
Specialist on epidemiology and social research

Kazuo SEIYAMA

Professor, Kwansei Gakuin University

Specialist on epidemiology and social research

Keiko SATO

Associate professor, Kyoto Unit Center Japan Environment & Children’s
Study, Graduate School of Medicine, Kyoto University

Specialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Principal, Tachikawa City Daiichi Elementary School

Research education staff of ISM

Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM)

Research education staff of ISM

Hiroe TSUBAKI

Professor ( Vice Director-General, ISM)

Research education staff of ISM

Shigeyuki MATSUI

Professor (Department of Data Science, ISM)

Research education staff of ISM

Tadahiko MAEDA

Associate Professor (Department of Data Science, ISM)

Professor Emeritus ( As of April 1, 2012)30

Kameo MATUSITA

Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

History

1944

June ● Based on a proposal submitted at an academic study conference in December 1943, the organization
was founded as an institute under the direct control of the Ministry of Education. This proposal aimed
to provide supervision for studies looking into the mathematical principles of probability and theirapplication, and was also intended to facilitate, unify and promote the publication of research results.

1947

April ● The affiliated statistical specialists’ school was opened.
May ● The Institute was divided into the 1st Research Dept. (fundamental theories), the 2nd Research Dept.
(statistical theories for the natural sciences), and the 3rd Research Dept. (statistical theories for thesocial sciences).

1949

June ● The Institute was placed under the control of the Ministry of Education because of the enforcement of
the Ministry of Education Establishment Law.

1955 September ● Reorganized into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (natural and social
science theories), and the 3rd Research Dept. (operations, research, statistical analysis theories). The
laboratory system, comprising 9 laboratories and the research guidance promotion room, was adopted.
1969

October ● A new office building was constructed in Minato Ward.

1971

April ● The 4th Research Dept. (informatics theories) was instituted.

1973

April ● The 5th Research Dept. (prediction and control theories) was instituted.

1975

October ● The 6th Research Dept. (statistical theories of human behavior) was instituted.

1979 November ● The Information Research Building was constructed.
1985

April ● Repositioned as an Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on statistical mathematics. Accordingly, the institute was reorganized into our basic research
departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics) and two strategic centers (Statistical Data Analysis Center and Statistical Education & Information Center). The Statistical Technical Training Center was terminated.

1988

October ● The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science,part of the Graduate University for Advanced Studies ( SOKENDAI ).

1989

June ● The Institute was reorganized as an Inter-University Research Institute based on the National School
Establishment Law.

1993

April ● The Planning Coordination Chief System was instituted.

1997

April ● The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into theCenter for Information on Statistical Sciences.

2003 September ● The Prediction and Knowledge Discovery Research Center was instituted.
2004

April ● The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation of Information and Systems of the Inter-University Research Institute based on the National
University Corporation Law. The Planning Coordination Chief System was abolished and the positionof Vice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathe­
matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Scienceand the School of Multidisciplinary Sciences were instituted.

2005

April ● The research organization was reorganized into three research departments ( the Department of Statis­
tical Modeling, the Department of Data Science, and the Department of Mathematical Analysis andStatistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for
Information on Statistical Sciences, and the Engineering and Technical Services Section were inte­
grated into the Center for Engineering and Technical Support. The Risk Analysis Research Center was
instituted.

2008

April ● The Research Innovation Center was instituted.
The Intellectual Property Unit was instituted.

2009
2010

October ● The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.
June ● Officially opened the Akaike Guest House.
July ● Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch
the General Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within the Administration Planning and Coordination Section.

2011

January ● Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January ● Research Center for Statistical Machine Learning, Service Science Research Center and School of
Statistical Thinking were instituted.

31

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorail—10 min walk from Takamatsu Sta.
Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/